---
license: cc-by-nc-sa-4.0
base_model: InstaDeepAI/nucleotide-transformer-v2-500m-multi-species
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: nucleotide-transformer-v2-500m-multi-species_ft_BioS74_1kbpHG19_DHSs_H3K27AC
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# nucleotide-transformer-v2-500m-multi-species_ft_BioS74_1kbpHG19_DHSs_H3K27AC

This model is a fine-tuned version of [InstaDeepAI/nucleotide-transformer-v2-500m-multi-species](https://huggingface.co/InstaDeepAI/nucleotide-transformer-v2-500m-multi-species) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0901
- F1 Score: 0.8408
- Precision: 0.8543
- Recall: 0.8277
- Accuracy: 0.8359
- Auc: 0.9180
- Prc: 0.9190

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | F1 Score | Precision | Recall | Accuracy | Auc    | Prc    |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.5159        | 0.2629 | 500  | 0.4336          | 0.8179   | 0.7917    | 0.8458 | 0.8028   | 0.8805 | 0.8705 |
| 0.4166        | 0.5258 | 1000 | 0.4006          | 0.8434   | 0.7908    | 0.9036 | 0.8243   | 0.9079 | 0.9051 |
| 0.3854        | 0.7886 | 1500 | 0.3993          | 0.8481   | 0.7691    | 0.9453 | 0.8228   | 0.9127 | 0.9072 |
| 0.3755        | 1.0515 | 2000 | 0.3677          | 0.8348   | 0.8600    | 0.8112 | 0.8320   | 0.9252 | 0.9258 |
| 0.2996        | 1.3144 | 2500 | 0.3570          | 0.8506   | 0.8478    | 0.8533 | 0.8430   | 0.9268 | 0.9291 |
| 0.2827        | 1.5773 | 3000 | 0.3578          | 0.8564   | 0.8504    | 0.8624 | 0.8485   | 0.9305 | 0.9303 |
| 0.285         | 1.8402 | 3500 | 0.3630          | 0.8386   | 0.8674    | 0.8117 | 0.8364   | 0.9270 | 0.9280 |
| 0.2425        | 2.1030 | 4000 | 0.9870          | 0.8391   | 0.8578    | 0.8212 | 0.8351   | 0.9202 | 0.9203 |
| 0.4127        | 2.3659 | 4500 | 2.0901          | 0.8408   | 0.8543    | 0.8277 | 0.8359   | 0.9180 | 0.9190 |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.0